Explore MATLAB source code curated for "目标" with clean implementations, documentation, and examples.
HIT's high-frequency surface wave radar simulation program capable of computing power spectral densities for target and ocean echoes, including noise analysis with MATLAB-based signal processing implementation.
MATLAB-based implementation of object extraction from video frames using frame difference method achieves superior extraction results through comparative analysis of consecutive frames
Simulation of single-station passive positioning algorithms for targets moving in two-dimensional linear trajectories, including code implementation details for motion modeling and signal processing.
MATLAB-based SAR point target simulation featuring clear program architecture and intuitive implementation, easily adaptable for educational and research purposes
A simulation program for targets and radar systems that allows configuration of target position vectors, velocity, radar cross-section, range gates, and incorporates Gaussian noise. The simulation performs mixing and Fourier transform operations to display range-Doppler two-dimensional plots with detailed signal processing implementation.
Linear Frequency Modulated (LFM) signals are electromagnetic waveforms commonly employed in radar systems. When radar signals interact with targets, they generate reflected echo signals that can be processed to extract target information.
Implement A-Star algorithm for optimal pathfinding to reach target destination with customizable obstacle configuration and successful execution
TDOA-based localization system using Kalman filter algorithm to reduce errors and achieve more accurate target tracking and positioning. Implementation includes state prediction, measurement update, and error covariance management for enhanced precision.
This research presents a path planning algorithm designed for target mobile robots operating in unknown environments. The algorithm enables autonomous navigation through static obstacles while finding collision-free paths to reach specified goals. Our approach allows the robot to move from initial positions to final target locations using grid-based mapping of unknown environments with static unknown obstacles. The robot achieves obstacle avoidance through remote sensing techniques while minimizing costs related to time, energy consumption, and travel distance. The proposed path planning strategy ensures the robot completes two primary tasks: avoiding obstacles and safely reaching its destination.
Multi-objective Particle Swarm Optimization Algorithm with Two Objective Functions Example